LPA and SC

COPE

We are interested in how people respond when they confront difficult or stressful events in their lives. There are lots of ways to try to deal with stress. This questionnaire asks you to indicate what you generally do and feel, when you experience stressful events. Obviously, different events bring out somewhat different responses, but think about what you usually do when you are under a lot of stress.

Then respond to each of the following items by blackening one number on your answer sheet for each, using the response choices listed just below. Please try to respond to each item separately in your mind from each other item. Choose your answers thoughtfully, and make your answers as true FOR YOU as you can. Please answer every item. There are no “right” or “wrong” answers, so choose the most accurate answer for YOU–not what you think “most people” would say or do. Indicate what YOU usually do when YOU experience a stressful event.

   1 = I usually don't  do this at all
   2 = I usually do this a little bit
   3 = I usually do this a medium amount
   4 = I usually do this a lot

1. I try to grow as a person as a result of the experience.

2. I turn to work or other substitute activities to take my mind off things.

3. I get upset and let my emotions out.

4. I try to get advice from someone about what to do.

5. I concentrate my efforts on doing something about it.

6. I say to myself “this isn’t real.”

7. I put my trust in God.

8. I laugh about the situation.

9. I admit to myself that I can’t deal with it, and quit trying.

10. I restrain myself from doing anything too quickly.

11. I discuss my feelings with someone.

12. I use alcohol or drugs to make myself feel better.

13. I get used to the idea that it happened.

14. I talk to someone to find out more about the situation.

15. I keep myself from getting distracted by other thoughts or activities.

16. I daydream about things other than this.

17. I get upset, and am really aware of it.

18. I seek God’s help.

19. I make a plan of action.

20. I make jokes about it.

21. I accept that this has happened and that it can’t be changed.

22. I hold off doing anything about it until the situation permits.

23. I try to get emotional support from friends or relatives.

24. I just give up trying to reach my goal.

25. I take additional action to try to get rid of the problem.

26. I try to lose myself for a while by drinking alcohol or taking drugs.

27. I refuse to believe that it has happened.

28. I let my feelings out.

29. I try to see it in a different light, to make it seem more positive.

30. I talk to someone who could do something concrete about the problem.

31. I sleep more than usual.

32. I try to come up with a strategy about what to do.

33. I focus on dealing with this problem, and if necessary let other things slide a little.

34. I get sympathy and understanding from someone.

35. I drink alcohol or take drugs, in order to think about it less.

36. I kid around about it.

37. I give up the attempt to get what I want.

38. I look for something good in what is happening.

39. I think about how I might best handle the problem.

40. I pretend that it hasn’t really happened.

41. I make sure not to make matters worse by acting too soon.

42. I try hard to prevent other things from interfering with my efforts at dealing with this.

43. I go to movies or watch TV, to think about it less.

44. I accept the reality of the fact that it happened.

45. I ask people who have had similar experiences what they did.

46. I feel a lot of emotional distress and I find myself expressing those feelings a lot.

47. I take direct action to get around the problem.

48. I try to find comfort in my religion.

49. I force myself to wait for the right time to do something.

50. I make fun of the situation.

51. I reduce the amount of effort I’m putting into solving the problem.

52. I talk to someone about how I feel.

53. I use alcohol or drugs to help me get through it.

54. I learn to live with it.

55. I put aside other activities in order to concentrate on this.

56. I think hard about what steps to take.

57. I act as though it hasn’t even happened.

58. I do what has to be done, one step at a time.

59. I learn something from the experience.

60. I pray more than usual.

---

Scales (sum items listed, with no reversals of coding):

Positive reinterpretation and growth: 1, 29, 38, 59 Mental disengagement: 2, 16, 31, 43 Focus on and venting of emotions: 3, 17, 28, 46 Use of instrumental social support: 4, 14, 30, 45 Active coping: 5, 25, 47, 58 Denial: 6, 27, 40, 57 Religious coping: 7, 18, 48, 60 Humor: 8, 20, 36, 50 Behavioral disengagement: 9, 24, 37, 51 Restraint: 10, 22, 41, 49 Use of emotional social support: 11, 23, 34, 52 Substance use: 12, 26, 35, 53 Acceptance: 13, 21, 44, 54 Suppression of competing activities: 15, 33, 42, 55 Planning: 19, 32, 39, 56

library("here")
library("tidyverse")
library("MplusAutomation")
library("gt")
library("glue")
library("kableExtra")
library("misty")
library("lavaan")
library("AICcmodavg")
library("nonnest2")
library("DiagrammeR")
library("lavaan")
library("tidyLPA")
library("semTools")
library("brms")
library("MBESS")
library("ufs")
library("robmed")
library("careless")
library("psych")

options("max.print" = .Machine$integer.max)

# Make random things reproducible
set.seed(1234)

options(
  mc.cores = 6 # Use 6 cores
)

source(here::here("src", "R", "functions", "funs_add_neoffi60_subscales.R"))
source(here::here("src", "R", "functions", "funs_correct_iesr_scores.R"))
source(here::here("src", "R", "functions", "funs_plot_job_qualification.R"))
source(here::here("src", "R", "functions", "funs_generate_all_items_df.R"))

scale_this <- function(x) as.vector(scale(x))

Get data

all_items <- generate_all_items_df()

There is a problem with IES-R, in the control group. I shift the control distribution of IES-R towards lower values.

all_items$ies_ts <- NULL
temp <- correct_iesr_scores(all_items)
all_items <- temp

Compute IES-R total score.

all_items$iesr_ts <- with(
  all_items,
  ies_1 + ies_2 + ies_3 + ies_4 + ies_5 + ies_6 + ies_7 + ies_8 + ies_9 + 
    ies_10 + ies_11 + ies_12 + ies_13 + ies_14 + ies_15 + ies_16 + ies_17 + 
    ies_18 + ies_19 + ies_20 + ies_21 + ies_22 
)

ggplot(all_items, aes(x = iesr_ts, colour = is_rescue_worker)) +
  geom_density()

all_items |> 
  group_by(is_rescue_worker) |> 
  summarize(
    avg_iesr = mean(iesr_ts)
  )

Supported families are: ‘acat’, ‘asym_laplace’, ‘bernoulli’, ‘beta’, ‘beta_binomial’, ‘binomial’, ‘categorical’, ‘com_poisson’, ‘cox’, ‘cratio’, ‘cumulative’, ‘custom’, ‘dirichlet’, ‘dirichlet2’, ‘discrete_weibull’, ‘exgaussian’, ‘exponential’, ‘frechet’, ‘gamma’, ‘gaussian’, ‘gen_extreme_value’, ‘geometric’, ‘hurdle_cumulative’, ‘hurdle_gamma’, ‘hurdle_lognormal’, ‘hurdle_negbinomial’, ‘hurdle_poisson’, ‘info’, ‘inverse.gaussian’, ‘logistic_normal’, ‘lognormal’, ‘multinomial’, ‘negbinomial’, ‘negbinomial2’, ‘poisson’, ‘shifted_lognormal’, ‘skew_normal’, ‘sratio’, ‘student’, ‘von_mises’, ‘weibull’, ‘wiener’, ‘zero_inflated_asym_laplace’, ‘zero_inflated_beta’, ‘zero_inflated_beta_binomial’, ‘zero_inflated_binomial’, ‘zero_inflated_negbinomial’, ‘zero_inflated_poisson’, ‘zero_one_inflated_beta’

The sk, ch, mi sub-scales are coded so that high values indicate high self-compassion levels. The sj, is, oi sub-scales are coded so that high values indicate low self-compassion levels.

The ts_sc score has been computed by reversing the coding of the items of the sj, is, oi sub-scales (so that they indicate the absence of self-judgment, absence of isolation, absence of over-identification).

scs_subscales <- with(all_items, data.frame(sk, ch, mi, sj, is, oi, ts_sc))
cor(scs_subscales) |> round(2)

         sk    ch    mi    sj    is    oi ts_sc
sk     1.00  0.52  0.58 -0.39 -0.28 -0.24  0.30
ch     0.52  1.00  0.49 -0.01 -0.03 -0.04  0.54
mi     0.58  0.49  1.00 -0.19 -0.33 -0.35  0.28
sj    -0.39 -0.01 -0.19  1.00  0.67  0.66  0.63
is    -0.28 -0.03 -0.33  0.67  1.00  0.80  0.67
oi    -0.24 -0.04 -0.35  0.66  0.80  1.00  0.67
ts_sc  0.30  0.54  0.28  0.63  0.67  0.67  1.00

COPE scale

In the COPE scale only two factors are identified.

all_items$pos_reinterpretation <- with(all_items, cope_1 + cope_29 + cope_38 + cope_59)
all_items$mental_disengagement <- with(all_items, cope_2 + cope_16 + cope_31 + cope_43) 
all_items$venting <- with(all_items, cope_3 + cope_17 + cope_28 + cope_46) 
all_items$seeking_instrumental_support <- with(all_items, cope_4 + cope_14 + cope_30 + cope_45) 
all_items$active_coping <- with(all_items, cope_5 + cope_25 + cope_47 + cope_58)  
all_items$denial <- with(all_items, cope_6 + cope_27 + cope_40 + cope_57) 
all_items$religion <- with(all_items, cope_7 + cope_18 + cope_48 + cope_60) 
all_items$humor <- with(all_items, cope_8 + cope_20 + cope_36 + cope_50) 
all_items$behavioral_disengagement <- with(all_items, cope_9 + cope_24 + cope_37 + cope_51) 
all_items$restraint <- with(all_items, cope_10 + cope_22 + cope_41 + cope_49) 
all_items$seeking_emotional_support <- with(all_items, cope_11 + cope_23 + cope_34 + cope_52) 
all_items$substance_use <- with(all_items, cope_12 + cope_26 + cope_35 + cope_53) 
all_items$acceptance <- with(all_items, cope_13 + cope_21 + cope_44 + cope_54) 
all_items$suppr_competing_activities <- with(all_items, cope_15 + cope_33 + cope_42 + cope_55) 
all_items$planning <- with(all_items, cope_19 + cope_32 + cope_39 + cope_56) 

Create COPE sub-scales scores using all items – note that SEM analyses suggest to drop some of the items.

all_items$active_coping <- with(
  all_items, pos_reinterpretation + active_coping +
  suppr_competing_activities + planning + restraint + 
    seeking_instrumental_support + acceptance
)

all_items$avoidance_coping <- with(
  all_items, mental_disengagement + denial + humor +
  behavioral_disengagement + substance_use + religion 
)

all_items$soc_emo_coping <- with(
  all_items, seeking_instrumental_support +
  seeking_emotional_support + venting
)

Self-compassion scale

library("brms")

plot(density(all_items$ts_sc))

fit_1 <- brm(
  ts_sc ~ is_rescue_worker,
  family = student(),
  backend = "cmdstanr",
  data = all_items
)

pp_check(fit_1)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

me <- conditional_effects(
  fit_1, "is_rescue_worker"
)
plot(me, points = FALSE)

summary(fit_1)

 Family: student 
  Links: mu = identity; sigma = identity; nu = identity 
Formula: ts_sc ~ is_rescue_worker 
   Data: all_items (Number of observations: 1068) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Population-Level Effects: 
                   Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept             74.07      0.45    73.19    74.93 1.00     3556     2618
is_rescue_workerno     6.94      0.80     5.41     8.53 1.00     3709     2912

Family Specific Parameters: 
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma    11.31      0.42    10.49    12.11 1.00     2512     2685
nu       12.50      5.27     6.55    25.64 1.00     2434     1949

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

LPA

lpa_scales <- c(
  # "is_rescue_worker",
  "neuroticism", "extraversion", "openness", "agreeableness", "conscientiousness",
  "active_coping", "avoidance_coping", "soc_emo_coping",
  "iesr_ts",
  # "sk", "ch", "mi", "sj", "is", "oi",
  # "pos_sc",
  # "neg_sc",
  # "ts_sc",
  "mpss_tot",
  "ptgi_total_score"
  # "relating_to_others",
  # "new_possibilities",
  # "personal_strength",
  # "appreciation_of_life",
  # "spirituality"
)

lpa_df <- subset(all_items, select=lpa_scales)

LPA

Only RW

rw_df <- all_items |> 
  dplyr::filter(is_rescue_worker == "yes")

lpa_rw_df <- subset(rw_df, select=lpa_scales)

I tried with only the RW data. The LPA produces a 2 classes solution. However, the classes are less interpretable than those found when the data of all two groups are used. So, it is better to use all the data for the LPA.

lpa_df %>% 
  scale() %>%
  estimate_profiles(1:10,
    variances = c("equal", "varying"),
    covariances = c("zero", "varying"),
    # package = "MplusAutomation"
  ) %>% 
  compare_solutions(statistics = c("AIC", "BIC"))

Compare tidyLPA solutions:

 Model Classes AIC       BIC      
 1     1       33372.375 33481.793
 1     2       32161.998 32331.098
 1     3       31651.493 31880.276
 1     4       31286.624 31575.089
 1     5       31144.365 31492.513
 1     6       31036.339 31444.170
 1     7       30886.942 31354.455
 1     8       30852.444 31379.640
 1     9       30730.207 31317.085
 1     10      30601.776 31248.336
 6     1       31156.833 31539.796
 6     2       30335.968 31106.867
 6     3       29932.341 31091.177
 6     4       29858.141 31404.913
 6     5       29793.858 31728.566
 6     6       29820.278 32142.922
 6     7       29695.429 32406.010
 6     8       29747.123 32845.640
 6     9       29727.921 33214.374
 6     10      29757.813 33632.203

Best model according to AIC is Model 6 with 7 classes.
Best model according to BIC is Model 6 with 3 classes.

An analytic hierarchy process, based on the fit indices AIC, AWE, BIC, CLC, and KIC (Akogul & Erisoglu, 2017), suggests the best solution is Model 6 with 3 classes.

Compare tidyLPA solutions:

Model Classes AIC BIC
1 1 86371.720 86481.034 1 2 85170.836 85339.776 1 3 84750.979 84979.546 1 4 84266.489 84554.683 1 5 84119.416 84467.235 1 6 84012.505 84419.951 1 7 83871.894 84338.966 1 8 83848.135 84374.833 1 9 83774.673 84360.997 1 10 83627.711 84273.661 6 1 84148.600 84531.202 6 2 83350.747 84120.919 6 3 82934.273 84092.015 6 4 82866.542 84411.855 6 5 82811.110 84743.992 6 6 82751.355 85071.809 6 7 82683.505 85391.528 6 8 82709.839 85805.433 6 9 82802.128 86285.292 6 10 82813.820 86684.554

Best model according to AIC is Model 6 with 7 classes. Best model according to BIC is Model 6 with 3 classes.

An analytic hierarchy process, based on the fit indices AIC, AWE, BIC, CLC, and KIC (Akogul & Erisoglu, 2017), suggests the best solution is Model 6 with 3 classes.

Varying variances and varying covariances (Model 6)

m2 <- lpa_df %>%
  # scale() %>%
  estimate_profiles(3,
    variances = "varying",
    covariances = "varying"
    # package = "MplusAutomation"
  )

temp <- lpa_df
temp$lpa_class <- NULL

m2_plot <- temp %>%
  scale() %>%
  estimate_profiles(2,
    variances = "varying",
    covariances = "varying",
    package = "MplusAutomation"
    ) %>%
    plot_profiles()

Profile 1: adaptive Profile 2: dysfunctional Profile 3: adaptive under duress (high IES scores, low MSPSS scores)

get_estimates(m2)

out <- get_data(m2)
lpa_df$lpa_class <- out$Class

table(
  lpa_df$lpa_class, all_items$is_rescue_worker
)

   
    yes  no
  1 249  47
  2  42 181
  3 455  94

1 - (455 + 249) / (455 + 249 + 42)

[1] 0.05630027

# [1] 0.9436997

181 / (181 + 47 + 94)

[1] 0.5621118

table(
  lpa_df$lpa_class, all_items$job_qualification
)

   
    driver team_leader team_member non_rescue_worker
  1     42         105         105                44
  2      3          17          18               185
  3    110         177         169                93

all_items$class <- factor(lpa_df$lpa_class)

m1 <- brm(
  ts_sc ~ class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m1)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

summary(m1)

 Family: gaussian 
  Links: mu = identity; sigma = identity 
Formula: ts_sc ~ class 
   Data: all_items (Number of observations: 1068) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Population-Level Effects: 
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept    80.25      0.68    78.88    81.59 1.00     3852     3014
class2        2.27      1.04     0.19     4.31 1.00     3990     3201
class3       -9.32      0.84   -10.94    -7.65 1.00     3962     3274

Family Specific Parameters: 
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma    11.81      0.25    11.34    12.33 1.00     3853     2863

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

me <- conditional_effects(
  m1, "class"
)
plot(me, points = FALSE)

names(all_items)

  [1] "ies_1"                          "ies_2"                          "ies_3"                         
  [4] "ies_4"                          "ies_5"                          "ies_6"                         
  [7] "ies_7"                          "ies_8"                          "ies_9"                         
 [10] "ies_10"                         "ies_11"                         "ies_12"                        
 [13] "ies_13"                         "ies_14"                         "ies_15"                        
 [16] "ies_16"                         "ies_17"                         "ies_18"                        
 [19] "ies_19"                         "ies_20"                         "ies_21"                        
 [22] "ies_22"                         "id"                             "group"                         
 [25] "avoiding"                       "intrusivity"                    "hyperarousal"                  
 [28] "ies_ts"                         "neoffi_1"                       "neoffi_2"                      
 [31] "neoffi_3"                       "neoffi_4"                       "neoffi_5"                      
 [34] "neoffi_6"                       "neoffi_7"                       "neoffi_8"                      
 [37] "neoffi_9"                       "neoffi_10"                      "neoffi_11"                     
 [40] "neoffi_12"                      "neoffi_13"                      "neoffi_14"                     
 [43] "neoffi_15"                      "neoffi_16"                      "neoffi_17"                     
 [46] "neoffi_18"                      "neoffi_19"                      "neoffi_20"                     
 [49] "neoffi_21"                      "neoffi_22"                      "neoffi_23"                     
 [52] "neoffi_24"                      "neoffi_25"                      "neoffi_26"                     
 [55] "neoffi_27"                      "neoffi_28"                      "neoffi_29"                     
 [58] "neoffi_30"                      "neoffi_31"                      "neoffi_32"                     
 [61] "neoffi_33"                      "neoffi_34"                      "neoffi_35"                     
 [64] "neoffi_36"                      "neoffi_37"                      "neoffi_38"                     
 [67] "neoffi_39"                      "neoffi_40"                      "neoffi_41"                     
 [70] "neoffi_42"                      "neoffi_43"                      "neoffi_44"                     
 [73] "neoffi_45"                      "neoffi_46"                      "neoffi_47"                     
 [76] "neoffi_48"                      "neoffi_49"                      "neoffi_50"                     
 [79] "neoffi_51"                      "neoffi_52"                      "neoffi_53"                     
 [82] "neoffi_54"                      "neoffi_55"                      "neoffi_56"                     
 [85] "neoffi_57"                      "neoffi_58"                      "neoffi_59"                     
 [88] "neoffi_60"                      "cope_1"                         "cope_2"                        
 [91] "cope_3"                         "cope_4"                         "cope_5"                        
 [94] "cope_6"                         "cope_7"                         "cope_8"                        
 [97] "cope_9"                         "cope_10"                        "cope_11"                       
[100] "cope_12"                        "cope_13"                        "cope_14"                       
[103] "cope_15"                        "cope_16"                        "cope_17"                       
[106] "cope_18"                        "cope_19"                        "cope_20"                       
[109] "cope_21"                        "cope_22"                        "cope_23"                       
[112] "cope_24"                        "cope_25"                        "cope_26"                       
[115] "cope_27"                        "cope_28"                        "cope_29"                       
[118] "cope_30"                        "cope_31"                        "cope_32"                       
[121] "cope_33"                        "cope_34"                        "cope_35"                       
[124] "cope_36"                        "cope_37"                        "cope_38"                       
[127] "cope_39"                        "cope_40"                        "cope_41"                       
[130] "cope_42"                        "cope_43"                        "cope_44"                       
[133] "cope_45"                        "cope_46"                        "cope_47"                       
[136] "cope_48"                        "cope_49"                        "cope_50"                       
[139] "cope_51"                        "cope_52"                        "cope_53"                       
[142] "cope_54"                        "cope_55"                        "cope_56"                       
[145] "cope_57"                        "cope_58"                        "cope_59"                       
[148] "cope_60"                        "ptgi_1"                         "ptgi_2"                        
[151] "ptgi_3"                         "ptgi_4"                         "ptgi_5"                        
[154] "ptgi_6"                         "ptgi_7"                         "ptgi_8"                        
[157] "ptgi_9"                         "ptgi_10"                        "ptgi_11"                       
[160] "ptgi_12"                        "ptgi_13"                        "ptgi_14"                       
[163] "ptgi_15"                        "ptgi_16"                        "ptgi_17"                       
[166] "ptgi_18"                        "ptgi_19"                        "ptgi_20"                       
[169] "ptgi_21"                        "scs_1"                          "scs_2"                         
[172] "scs_3"                          "scs_4"                          "scs_5"                         
[175] "scs_6"                          "scs_7"                          "scs_8"                         
[178] "scs_9"                          "scs_10"                         "scs_11"                        
[181] "scs_12"                         "scs_13"                         "scs_14"                        
[184] "scs_15"                         "scs_16"                         "scs_17"                        
[187] "scs_18"                         "scs_19"                         "scs_20"                        
[190] "scs_21"                         "scs_22"                         "scs_23"                        
[193] "scs_24"                         "scs_25"                         "scs_26"                        
[196] "mspss_1"                        "mspss_2"                        "mspss_3"                       
[199] "mspss_4"                        "mspss_5"                        "mspss_6"                       
[202] "mspss_7"                        "mspss_8"                        "mspss_9"                       
[205] "mspss_10"                       "mspss_11"                       "mspss_12"                      
[208] "date"                           "gender"                         "age"                           
[211] "education"                      "employment"                     "is_rescue_worker"              
[214] "red_cross_commeetee_location"   "rescue_worker_qualification"    "last_training"                 
[217] "rate_of_activity"               "job_qualification"              "is_job_qualification_invariant"
[220] "is_team_invariant"              "is_married"                     "FLAG_1"                        
[223] "neuroticism"                    "extraversion"                   "openness"                      
[226] "agreeableness"                  "conscientiousness"              "social_support"                
[229] "avoiding_strategies"            "positive_attitude"              "problem_orientation"           
[232] "transcendent_orientation"       "cope_total_score"               "relating_to_others"            
[235] "new_possibilities"              "personal_strength"              "appreciation_of_life"          
[238] "spirituality"                   "ptgi_total_score"               "self_kindness"                 
[241] "self_judgment"                  "common_humanity"                "isolation"                     
[244] "mindfulness"                    "over_identification"            "neg_self_compassion"           
[247] "pos_self_compassion"            "family"                         "friends"                       
[250] "significant_other"              "mpss_tot"                       "FLAG_2"                        
[253] "negative_affect"                "self_reproach"                  "positive_affect"               
[256] "sociability"                    "activity"                       "aesthetic_interests"           
[259] "intellectual_interests"         "unconventionality"              "nonantagonistic_orientation"   
[262] "prosocial_orientation"          "orderliness"                    "goal_striving"                 
[265] "dependability"                  "rate_of_activity_num"           "last_training_num"             
[268] "education_num"                  "pos_sc"                         "neg_sc"                        
[271] "ts_sc"                          "sk"                             "ch"                            
[274] "mi"                             "sj"                             "is"                            
[277] "oi"                             "iesr_ts"                        "pos_reinterpretation"          
[280] "mental_disengagement"           "venting"                        "seeking_instrumental_support"  
[283] "active_coping"                  "denial"                         "religion"                      
[286] "humor"                          "behavioral_disengagement"       "restraint"                     
[289] "seeking_emotional_support"      "substance_use"                  "acceptance"                    
[292] "suppr_competing_activities"     "planning"                       "avoidance_coping"              
[295] "soc_emo_coping"                 "class"                         

m2 <- brm(
  sj ~ job_qualification * class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m2)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

summary(m2)

 Family: gaussian 
  Links: mu = identity; sigma = identity 
Formula: sj ~ job_qualification * class 
   Data: all_items (Number of observations: 1068) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Population-Level Effects: 
                                          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept                                    16.09      0.70    14.72    17.49 1.00     1489
job_qualificationteam_leader                  1.24      0.84    -0.47     2.88 1.00     1660
job_qualificationteam_member                  1.25      0.83    -0.35     2.87 1.00     1586
job_qualificationnon_rescue_worker            0.64      1.00    -1.33     2.64 1.00     1774
class2                                       -2.30      2.74    -7.53     3.02 1.00     1550
class3                                       -3.15      0.83    -4.83    -1.55 1.00     1490
job_qualificationteam_leader:class2           1.14      3.01    -4.62     6.92 1.00     1677
job_qualificationteam_member:class2           4.03      2.98    -1.88     9.84 1.00     1643
job_qualificationnon_rescue_worker:class2     2.05      2.85    -3.52     7.44 1.00     1569
job_qualificationteam_leader:class3          -0.09      1.00    -1.98     1.85 1.00     1658
job_qualificationteam_member:class3          -0.35      1.00    -2.29     1.58 1.00     1583
job_qualificationnon_rescue_worker:class3     1.94      1.19    -0.52     4.27 1.00     1691
                                          Tail_ESS
Intercept                                     1881
job_qualificationteam_leader                  2092
job_qualificationteam_member                  2121
job_qualificationnon_rescue_worker            2245
class2                                        2027
class3                                        2186
job_qualificationteam_leader:class2           2128
job_qualificationteam_member:class2           1902
job_qualificationnon_rescue_worker:class2     2035
job_qualificationteam_leader:class3           2472
job_qualificationteam_member:class3           2443
job_qualificationnon_rescue_worker:class3     2389

Family Specific Parameters: 
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma     4.52      0.10     4.33     4.71 1.00     2971     2656

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

me <- conditional_effects(
  m2, "job_qualification:class"
)
plot(me, points = FALSE)

m3 <- brm(
  is ~ job_qualification * class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m3)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

me <- conditional_effects(
  m3, "job_qualification:class"
)
plot(me, points = FALSE)

m4 <- brm(
  oi ~ job_qualification*class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m4)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

me <- conditional_effects(
  m4, "job_qualification:class"
)
plot(me, points = FALSE)

m5 <- brm(
  sk ~ job_qualification*class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m5)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

me <- conditional_effects(
  m5, "job_qualification:class"
)
plot(me, points = FALSE)

m6 <- brm(
  ch ~ job_qualification*class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m6)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

me <- conditional_effects(
  m6, "job_qualification:class"
)
plot(me, points = FALSE)

m7 <- brm(
  mi ~ job_qualification*class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m7)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

me <- conditional_effects(
  m7, "job_qualification:class"
)
plot(me, points = FALSE)

Profilo 2: disfunzionale Profilo 1: adattivo Profilo 3: adattivo con stress

m8 <- brm(
  sj ~ class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m8)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

me <- conditional_effects(
  m8, "class"
)
plot(me, points = FALSE)

m9 <- brm(
  is ~ class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m9)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

me <- conditional_effects(
  m9, "class"
)
plot(me, points = FALSE)

m10 <- brm(
  oi ~ class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m10)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

me <- conditional_effects(
  m10, "class"
)
plot(me, points = FALSE)

m11 <- brm(
  sk ~ class,
  data = all_items,
  backend = "cmdstanr",
)

pp_check(m11)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

me <- conditional_effects(
  m11, "class"
)
plot(me, points = FALSE)

Ordinal regression

rw_df <- all_items |> 
  dplyr::filter(is_rescue_worker == "yes")

rw_df$job_qualification <- factor(
  rw_df$job_qualification,
  order = TRUE,
  levels = c("driver", "team_leader", "team_member")
)

fit_sc1 <- brm(
formula = job_qualification ~ 1 + class, data = rw_df,
family = cumulative("probit")
)

Warning: Rows containing NAs were excluded from the model.Compiling Stan program...
Start sampling

starting worker pid=61591 on localhost:11543 at 16:59:32.866
starting worker pid=61605 on localhost:11543 at 16:59:33.035
starting worker pid=61619 on localhost:11543 at 16:59:33.193
starting worker pid=61633 on localhost:11543 at 16:59:33.348

SAMPLING FOR MODEL 'c7b1a3fcb4cf4364ae59a68a5be2c6cb' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.0002 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)

SAMPLING FOR MODEL 'c7b1a3fcb4cf4364ae59a68a5be2c6cb' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 0.000187 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.87 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
Chain 2: 
Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)

SAMPLING FOR MODEL 'c7b1a3fcb4cf4364ae59a68a5be2c6cb' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 0.000169 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.69 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3: 
Chain 3: 
Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)

SAMPLING FOR MODEL 'c7b1a3fcb4cf4364ae59a68a5be2c6cb' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 0.000183 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.83 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4: 
Chain 4: 
Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)
Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)
Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)
Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)
Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)
Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 1: 
Chain 1:  Elapsed Time: 0.940123 seconds (Warm-up)
Chain 1:                0.837873 seconds (Sampling)
Chain 1:                1.778 seconds (Total)
Chain 1: 
Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 2: 
Chain 2:  Elapsed Time: 0.932211 seconds (Warm-up)
Chain 2:                0.866921 seconds (Sampling)
Chain 2:                1.79913 seconds (Total)
Chain 2: 
Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 3: 
Chain 3:  Elapsed Time: 0.923712 seconds (Warm-up)
Chain 3:                0.796903 seconds (Sampling)
Chain 3:                1.72062 seconds (Total)
Chain 3: 
Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 4: 
Chain 4:  Elapsed Time: 0.956639 seconds (Warm-up)
Chain 4:                0.808723 seconds (Sampling)
Chain 4:                1.76536 seconds (Total)
Chain 4: 

marginal_effects(fit_sc1, "class", categorical = TRUE)

Warning: Method 'marginal_effects' is deprecated. Please use 'conditional_effects' instead.

summary(fit_sc1)

 Family: cumulative 
  Links: mu = probit; disc = identity 
Formula: job_qualification ~ 1 + class 
   Data: rw_df (Number of observations: 741) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Population-Level Effects: 
             Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1]    -0.93      0.08    -1.08    -0.78 1.00     2887     2519
Intercept[2]     0.17      0.07     0.03     0.32 1.00     3867     3380
class2           0.18      0.20    -0.21     0.58 1.00     3350     2858
class3          -0.19      0.09    -0.36    -0.02 1.00     3517     3061

Family Specific Parameters: 
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

all_items_clean <- all_items[!temp1$FLAG, ]

all_items_clean$class

   [1] 0 1 0 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 0 1 1 1 1 0 0 0 1 0 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 1 1 0 1 1
  [62] 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0
 [123] 1 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 0 0 1 1 0 1 1 1 0 0 1
 [184] 1 0 1 0 0 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1 0 1 0 0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1 1 1 1 1 1 1 0 1 1
 [245] 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0
 [306] 1 1 0 0 1 0 1 1 0 1 0 1 1 0 0 0 1 0 1 0 1 1 0 1 0 1 1 0 1 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 0 1 1 0 1 0 1 0 1 1 1 0 1 1 1
 [367] 1 1 0 1 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 0 1 1 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 0 0 1 1 1 1 1 0 1 0 1
 [428] 0 0 0 1 1 1 1 0 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 1 0 0 0 1 1 1 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0
 [489] 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 0 1 1
 [550] 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0 0 0 1 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 0 1 1 0 0 0 1
 [611] 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 0 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0
 [672] 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 1 0 1 1 0 0 1 1 0 1 0 0 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 0 1 1 1
 [733] 1 1 0 0 0 1 0 1 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 0
 [794] 0 1 1 1 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0
 [855] 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1 1 0 0
 [916] 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1
 [977] 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 1 0 1 0 0 0 0 0 1
[1038] 1 0 0 0 0 0 0 1 0 1 0 0 1 1 1 1 0 1 1 0 1 0 0

m1 <- brm(
  class ~ is_rescue_worker,
  family = bernoulli(),
  backend = "cmdstanr",
  all_items_clean
)

pp_check(m1)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

summary(m1)

 Family: bernoulli 
  Links: mu = logit 
Formula: class ~ is_rescue_worker 
   Data: all_items_clean (Number of observations: 1060) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Population-Level Effects: 
                   Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept              0.72      0.08     0.56     0.88 1.00     2948     2459
is_rescue_workerno    -1.49      0.15    -1.79    -1.21 1.00     2718     2153

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

conditional_effects(
  m1, "is_rescue_worker"
)

model_glm = glm(class ~ is_rescue_worker, data = all_items_clean, family = "binomial")

model_glm_pred = ifelse(predict(model_glm, type = "link") > 0, "Yes", "No")

all_items_clean$class_lpa <- ifelse(all_items_clean$class == 1, "Yes", "No")

train_tab = table(predicted = model_glm_pred, actual = all_items_clean$class_lpa)
library(caret)
train_con_mat = confusionMatrix(train_tab, positive = "Yes")
c(train_con_mat$overall["Accuracy"], 
  train_con_mat$byClass["Sensitivity"], 
  train_con_mat$byClass["Specificity"])

   Accuracy Sensitivity Specificity 
  0.6754717   0.8333333   0.4695652 

library(pROC)
test_prob = predict(model_glm, newdata = all_items_clean, type = "response")

all_items_clean$rw <- ifelse(
  all_items_clean$is_rescue_worker == "yes", 1, 0
)

test_roc = roc(all_items_clean$class_lpa ~ all_items_clean$rw, plot = TRUE, print.auc = TRUE)

Setting levels: control = No, case = Yes
Setting direction: controls < cases

as.numeric(test_roc$auc)

[1] 0.6514493

summary(model_glm)

Call:
glm(formula = class ~ is_rescue_worker, family = "binomial", 
    data = all_items_clean)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4932  -0.8723   0.8915   0.8915   1.5170  

Coefficients:
                   Estimate Std. Error z value Pr(>|z|)    
(Intercept)         0.71744    0.07809   9.187   <2e-16 ***
is_rescue_workerno -1.48755    0.14397 -10.332   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1450.9  on 1059  degrees of freedom
Residual deviance: 1336.0  on 1058  degrees of freedom
AIC: 1340

Number of Fisher Scoring iterations: 4

all_items$last_training <- ifelse(
  is.na(all_items$last_training), 0, all_items$last_training
)

all_items$rate_of_activity <- ifelse(
  is.na(all_items$rate_of_activity), 0, all_items$rate_of_activity
)

all_items$is_job_qualification_invariant <- ifelse(
  all_items$is_job_qualification_invariant == "Sì" | all_items$is_job_qualification_invariant == "Si", 
  "Yes", all_items$is_job_qualification_invariant
)

all_items$is_job_qualification_invariant <- ifelse(
  is.na(all_items$is_job_qualification_invariant), "Cntr", all_items$is_job_qualification_invariant
)

all_items$is_team_invariant <- ifelse(
  all_items$is_team_invariant == "Sì" | all_items$is_team_invariant == "Si", 
  "Yes", all_items$is_team_invariant
)

all_items$is_team_invariant <- ifelse(
  is.na(all_items$is_team_invariant), "Cntr", all_items$is_team_invariant
)

rw_df <- all_items |> 
  dplyr::filter(is_rescue_worker == "yes" & job_qualification != "non_rescue_worker")

fm1 <- brm(
  class ~ job_qualification,
    # (gender + age + education + employment + job_qualification),
    # last_training + rate_of_activity + is_job_qualification_invariant + is_team_invariant),
  family = bernoulli(),
  backend = "cmdstanr",
  rw_df
)

pp_check(fm1)

Using 10 posterior draws for ppc type 'dens_overlay' by default.

summary(fm1)

 Family: bernoulli 
  Links: mu = logit 
Formula: class ~ job_qualification 
   Data: rw_df (Number of observations: 741) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Population-Level Effects: 
                             Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept                        1.55      0.21     1.16     1.98 1.00     1869     2049
job_qualificationteam_leader    -0.72      0.24    -1.20    -0.24 1.00     2054     2406
job_qualificationteam_member    -0.90      0.24    -1.39    -0.44 1.00     2121     2265

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

conditional_effects(
  fm1, "job_qualification"
)

fm2 <- brm(
  class ~ gender + age + education,
  family = bernoulli(),
  backend = "cmdstanr",
  rw_df
)

summary(fm2)

 Family: bernoulli 
  Links: mu = logit 
Formula: class ~ gender + age + education 
   Data: rw_df (Number of observations: 741) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Population-Level Effects: 
                             Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept                       -0.37      0.27    -0.90     0.18 1.00     6772     3014
gender1                         -0.20      0.08    -0.36    -0.03 1.00     6537     3326
age                              0.03      0.01     0.02     0.04 1.00     6878     3506
educationDottorato               0.37      0.72    -0.88     1.93 1.00     6791     2559
educationLaureabreve             0.01      0.24    -0.46     0.49 1.00     5725     3185
educationLaureamagistrale        0.50      0.23     0.07     0.94 1.00     5968     3109
educationScuolamediaprimaria     0.19      0.36    -0.48     0.95 1.00     6815     2645

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

EOF ——————-

suppressMessages(mod_1c_v1 <- estimate_profiles(df = lpa_df, n_profiles = 1:9,
models = 1))

mod_1c_v1

only_rw <- all_items |> 
  dplyr::filter(is_rescue_worker == "yes")

rw_df <- data.frame(
  # exogeneous vars
  neu = only_rw$neuroticism, 
  extra = only_rw$extraversion,
  open = only_rw$openness,
  agree = only_rw$agreeableness,
  consc = only_rw$conscientiousness,
  mpss = only_rw$mpss_tot,
  # mediators
  actcop = only_rw$active_coping,
  avocop = only_rw$avoidance_coping,
  scpos = only_rw$pos_self_compassion,
  scneg = only_rw$neg_self_compassion,
  # endogeneous vars
  iesr = only_rw$iesr_ts,
  ptgi = only_rw$ptgi_total_score
)

mod1 <- iesr ~ serial_m(actcop, scneg) + mpss + covariates(neu, consc)

out1 <- test_mediation(mod1, data = rw_df, robust= FALSE)

summary(out1)

Bootstrap tests for indirect effects via regression

Serial multiple mediator model

x  = mpss
y  = iesr
m1 = actcop
m2 = scneg

Covariates:
[1] neu   consc

Sample size: 746
---
Outcome variable: actcop

Coefficients:
                Data     Boot Std. Error z value Pr(>|z|)    
(Intercept) 33.09508 33.13128    1.94302  17.051  < 2e-16 ***
mpss         0.06599  0.06546    0.01974   3.317 0.000910 ***
neu         -0.10798 -0.10860    0.02893  -3.754 0.000174 ***
consc        0.33859  0.33870    0.03790   8.937  < 2e-16 ***

Residual standard error: 6.109 on 742 degrees of freedom
Multiple R-squared:  0.1983,    Adjusted R-squared:  0.1951
F-statistic: 61.19 on 3 and 742 DF,  p-value: < 2.2e-16
---
Outcome variable: scneg

Coefficients:
                Data     Boot Std. Error z value Pr(>|z|)    
(Intercept) 65.98252 65.96909    3.36814  19.586   <2e-16 ***
actcop      -0.22981 -0.23060    0.05194  -4.440    9e-06 ***
mpss         0.06306  0.06327    0.02750   2.301   0.0214 *  
neu         -0.90687 -0.90724    0.03892 -23.309   <2e-16 ***
consc        0.03896  0.04041    0.05829   0.693   0.4882    

Residual standard error: 8.474 on 741 degrees of freedom
Multiple R-squared:  0.4726,    Adjusted R-squared:  0.4698
F-statistic:   166 on 4 and 741 DF,  p-value: < 2.2e-16
---
Outcome variable: iesr

Coefficients:
                Data     Boot Std. Error z value Pr(>|z|)    
(Intercept) 16.39604 16.43346    6.56136   2.505  0.01226 *  
actcop       0.06099  0.06125    0.08823   0.694  0.48754    
scneg       -0.45999 -0.45999    0.06182  -7.441 1.00e-13 ***
mpss         0.04885  0.04776    0.04232   1.129  0.25901    
neu          0.35611  0.35623    0.08465   4.208 2.57e-05 ***
consc        0.25855  0.25891    0.09551   2.711  0.00671 ** 

Residual standard error: 13.58 on 740 degrees of freedom
Multiple R-squared:  0.228, Adjusted R-squared:  0.2228
F-statistic: 43.71 on 5 and 740 DF,  p-value: < 2.2e-16
---
Total effect of x on y:
        Data    Boot Std. Error z value Pr(>|z|)
mpss 0.03085 0.02965    0.04369   0.679    0.497

Direct effect of x on y:
        Data    Boot Std. Error z value Pr(>|z|)
mpss 0.04885 0.04776    0.04232   1.129    0.259

Indirect effects of x on y:
               Data      Boot     Lower     Upper
Total     -0.018005 -0.018113 -0.048665  0.011358
Indirect1  0.004025  0.004089 -0.006955  0.018397
Indirect2 -0.029005 -0.029200 -0.057280 -0.003639
Indirect3  0.006976  0.006998  0.002613  0.014885

Indirect effect paths:

---
Level of confidence: 95 %

Number of bootstrap replicates: 5000
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

mod_med <- '

  ptgi_total_score ~ neuroticism       
  ptgi_total_score ~ extraversion      
  ptgi_total_score ~ openness          
  ptgi_total_score ~ agreeableness    
  ptgi_total_score ~ conscientiousness
  ptgi_total_score ~ mpss_tot         
  
  iesr_ts ~ neuroticism     
  iesr_ts ~ extraversion       
  iesr_ts ~ openness           
  iesr_ts ~ agreeableness     
  iesr_ts ~ conscientiousness 
  iesr_ts ~ mpss_tot          
  
  active_coping ~ neuroticism       
  active_coping ~ extraversion    
  active_coping ~ openness        
  active_coping ~ agreeableness   
  active_coping ~ conscientiousness
  active_coping ~ mpss_tot        
  
  avoidance_coping ~ neuroticism       
  avoidance_coping ~ extraversion     
  avoidance_coping ~ openness         
  avoidance_coping ~ agreeableness    
  avoidance_coping ~ conscientiousness
  avoidance_coping ~ mpss_tot  
  
  ts_sc ~ neuroticism       
  ts_sc ~ extraversion     
  ts_sc ~ openness         
  ts_sc ~ agreeableness    
  ts_sc ~ conscientiousness
  ts_sc ~ mpss_tot     
  
  ptgi_total_score ~ ts_sc
  ptgi_total_score ~ active_coping
  ptgi_total_score ~ avoidance_coping
  
  iesr_ts ~ ts_sc
  iesr_ts ~ active_coping
  iesr_ts ~ avoidance_coping
'

mod_med <- '


  pos_self_compassion ~ c(pscn1, pscn2)*neuroticism + c(psce1, psce2)*extraversion + c(psco1, psco2)*openness + 
                        c(psca1, psca2)*agreeableness + c(pscc1, pscc2)*conscientiousness + c(pscm1, pscm2)*mpss_tot + 
                        c(psct1, psct2)*active_coping + c(pscv1, pscv2)*avoidance_coping
    
  neg_self_compassion ~ c(nscn1, nscn2)*neuroticism + c(nsce1, nce2)*extraversion + c(nsco1, nsco2)*openness + 
                        c(nsca1, nsca2)*agreeableness + c(nscc1, nscc2)*conscientiousness + c(nscm1, nscm2)*mpss_tot + 
                        c(nscat1, nsct2)*active_coping + c(nscv1, nscv2)*avoidance_coping 

  ptgi_total_score ~ c(gn1, gn2)*neuroticism + c(ge1, ge2)*extraversion + c(go1, go2)*openness + 
                        c(ga1, ga2)*agreeableness + c(gc1, gc2)*conscientiousness + c(gm1, gm2)*mpss_tot + 
                        c(gat1, gat2)*active_coping + c(gv1, gv2)*avoidance_coping
                     
  iesr_ts          ~ c(in1, in2)*neuroticism + c(ie1, ie2)*extraversion + c(io1, io2)*openness + 
                        c(ia1, ia2)*agreeableness + c(ic1, ic2)*conscientiousness + c(im1, im2)*mpss_tot + 
                        c(iat1, iat2)*active_coping + c(iv1, iv2)*avoidance_coping
  
  ptgi_total_score ~ c(gpsc1, gpsc2)*pos_self_compassion + c(gnsc1, gnsc2)*neg_self_compassion
  iesr_ts          ~ c(ipsc1, ipsc2)*pos_self_compassion + c(insc1, insc2)*neg_self_compassion
  
  pos_self_compassion ~~ neg_self_compassion
  
  # indirect effect ()
  abgg1 := pscn1*gpsc1 + psce1*gpsc1 + psco1*gpsc1 + psca1*gpsc1 + pscc1*gpsc1 + pscm1*gpsc1 + psct1*gpsc1 + pscv1*gpsc1 
  abgg2 := pscn2*gpsc2 + psce2*gpsc2 + psco2*gpsc2 + psca2*gpsc2 + pscc2*gpsc2 + pscm2*gpsc2 + psct2*gpsc2 + pscv2*gpsc2 
  
  abig1 := pscn1*ipsc1 + psce1*ipsc1 + psco1*ipsc1 + psca1*ipsc1 + pscc1*ipsc1 + pscm1*ipsc1 + psct1*ipsc1 + pscv1*ipsc1 
  abig2 := pscn2*ipsc2 + psce2*ipsc2 + psco2*ipsc2 + psca2*ipsc2 + pscc2*ipsc2 + pscm2*ipsc2 + psct2*ipsc2 + pscv2*ipsc2 
  
  # total effect
  totalgg1 := abgg1 + gn1 + ge1 + go1 + ga1 + gc1 + gm1 + gat1 + gv1
  totalgg2 := abgg2 + gn2 + ge2 + go2 + ga2 + gc2 + gm2 + gat2 + gv2
  totalig1 := abig1 + in1 + ie1 + io1 + ia1 + ic1 + im1 + iat1 + iv1
  totalig2 := abig2 + in2 + ie2 + io2 + ia2 + ic2 + im2 + iat2 + iv2
  
'

mod_med <- '

  ptgi_total_score ~ c(gn1, gn2)*neuroticism + c(ge1, ge2)*extraversion + c(go1, go2)*openness + 
                        c(ga1, ga2)*agreeableness + c(gc1, gc2)*conscientiousness + c(gm1, gm2)*mpss_tot 
                        
  iesr_ts          ~ c(in1, in2)*neuroticism + c(ie1, ie2)*extraversion + c(io1, io2)*openness + 
                        c(ia1, ia2)*agreeableness + c(ic1, ic2)*conscientiousness + c(im1, im2)*mpss_tot 
  
  
                        
  pos_self_compassion ~ c(pscn1, pscn2)*neuroticism + c(psce1, psce2)*extraversion + c(psco1, psco2)*openness + 
                        c(psca1, psca2)*agreeableness + c(pscc1, pscc2)*conscientiousness + c(pscm1, pscm2)*mpss_tot 
    
  neg_self_compassion ~ c(nscn1, nscn2)*neuroticism + c(nsce1, nsce2)*extraversion + c(nsco1, nsco2)*openness + 
                        c(nsca1, nsca2)*agreeableness + c(nscc1, nscc2)*conscientiousness + c(nscm1, nscm2)*mpss_tot 

  active_coping ~ c(acn1, acn2)*neuroticism + c(ace1, ace2)*extraversion + c(aco1, aco2)*openness + 
                        c(aca1, aca2)*agreeableness + c(acc1, acc2)*conscientiousness + c(acm1, acm2)*mpss_tot 
                        
  avoidance_coping ~ c(vcn1, vcn2)*neuroticism + c(vce1, vce2)*extraversion + c(vco1, vco2)*openness + 
                        c(vca1, vca2)*agreeableness + c(vcc1, vcc2)*conscientiousness + c(vcm1, vcm2)*mpss_tot 



  ptgi_total_score ~ c(gpsc1, gpsc2)*pos_self_compassion + c(gnsc1, gnsc2)*neg_self_compassion +
                     c(gac1, gac2)*active_coping + c(gav1, gav2)*avoidance_coping
                     
  iesr_ts          ~ c(ipsc1, ipsc2)*pos_self_compassion + c(insc1, insc2)*neg_self_compassion +
                     c(iac1, iac2)*active_coping + c(iav1, iav2)*avoidance_coping
  
  pos_self_compassion ~~ neg_self_compassion
  
  # indirect effect 
  abgg1 := pscn1*gpsc1 + psce1*gpsc1 + psco1*gpsc1 + psca1*gpsc1 + pscc1*gpsc1 + 
           pscm1*gpsc1 +
           nscn1*gpsc1 + nsce1*gpsc1 + nsco1*gpsc1 + nsca1*gpsc1 + nscc1*gpsc1 + 
           nscm1*gpsc1 +
           acn1*gpsc1 + ace1*gpsc1 + aco1*gpsc1 + aca1*gpsc1 + acc1*gpsc1 + 
           acm1*gpsc1 +
           vcn1*gpsc1 + vce1*gpsc1 + vco1*gpsc1 + vca1*gpsc1 + vcc1*gpsc1 + 
           vcm1*gpsc1 
           
  abgg2 := pscn2*gpsc2 + psce2*gpsc2 + psco2*gpsc2 + psca2*gpsc2 + pscc2*gpsc2 + 
           pscm2*gpsc2 +
           nscn2*gpsc2 + nsce2*gpsc2 + nsco2*gpsc2 + nsca2*gpsc2 + nscc2*gpsc2 +
           nscm2*gpsc2 +
           acn2*gpsc2 + ace2*gpsc2 + aco2*gpsc2 + aca2*gpsc2 + acc2*gpsc2 + 
           acm2*gpsc2 +
           vcn2*gpsc2 + vce2*gpsc2 + vco2*gpsc2 + vca2*gpsc2 + vcc2*gpsc2 + 
           vcm2*gpsc2 
   
   abig1 := pscn1*ipsc1 + psce1*ipsc1 + psco1*ipsc1 + psca1*ipsc1 + pscc1*ipsc1 + 
           pscm1*ipsc1 +
           nscn1*ipsc1 + nsce1*ipsc1 + nsco1*ipsc1 + nsca1*ipsc1 + nscc1*ipsc1 + 
           nscm1*ipsc1 +
           acn1*ipsc1 + ace1*ipsc1 + aco1*ipsc1 + aca1*ipsc1 + acc1*ipsc1 + 
           acm1*ipsc1 +
           vcn1*ipsc1 + vce1*ipsc1 + vco1*ipsc1 + vca1*ipsc1 + vcc1*ipsc1 + 
           vcm1*ipsc1 
                   
 abig2 := pscn2*ipsc2 + psce2*ipsc2 + psco2*ipsc2 + psca2*ipsc2 + pscc2*ipsc2 +  
          pscm2*ipsc2 +
          nscn2*ipsc2 + nsce2*ipsc2 + nsco2*ipsc2 + nsca2*ipsc2 + nscc2*ipsc2 +
          nscm2*ipsc2 +
          acn2*ipsc2 + ace2*ipsc2 + aco2*ipsc2 + aca2*ipsc2 + acc2*ipsc2 + 
          acm2*ipsc2 +
          vcn2*ipsc2 + vce2*ipsc2 + vco2*ipsc2 + vca2*ipsc2 + vcc2*ipsc2 + 
          vcm2*ipsc2 
                  
  
  # total effect
    totalgg1 := abgg1 + gn1 + ge1 + go1 + ga1 + gc1 + gm1 + gac1 + gav1
    totalgg2 := abgg2 + gn2 + ge2 + go2 + ga2 + gc2 + gm2 + gac2 + gav2
    
    totalig1 := abig1 + in1 + ie1 + io1 + ia1 + ic1 + im1 + iac1 + iav1
    totalig2 := abig2 + in2 + ie2 + io2 + ia2 + ic2 + im2 + iac2 + iav2
  
'

all_items$neg_self_compassion <- -1 * all_items$neg_self_compassion 

fit2 <- sem(mod_med, data = all_items, group = "is_rescue_worker", 
            se = "bootstrap",
           bootstrap = 5000,
           parallel ="snow", ncpus = 8)

standardizedSolution_boot_ci(fit2)

modificationindices(fit2, sort = TRUE)

summary(fit2,  fit.measures=TRUE, standardized = TRUE)

lavaan 0.6.15 ended normally after 303 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of model parameters                       116

  Number of observations per group:                   
    yes                                            746
    no                                             322

Model Test User Model:
                                                      
  Test statistic                               237.485
  Degrees of freedom                                10
  P-value (Chi-square)                           0.000
  Test statistic for each group:
    yes                                        167.335
    no                                          70.149

Model Test Baseline Model:

  Test statistic                              1891.499
  Degrees of freedom                               102
  P-value                                        0.000

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.873
  Tucker-Lewis Index (TLI)                      -0.297

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)             -23593.726
  Loglikelihood unrestricted model (H1)     -23474.983
                                                      
  Akaike (AIC)                               47419.451
  Bayesian (BIC)                             47996.382
  Sample-size adjusted Bayesian (SABIC)      47627.945

Root Mean Square Error of Approximation:

  RMSEA                                          0.206
  90 Percent confidence interval - lower         0.184
  90 Percent confidence interval - upper         0.230
  P-value H_0: RMSEA <= 0.050                    0.000
  P-value H_0: RMSEA >= 0.080                    1.000

Standardized Root Mean Square Residual:

  SRMR                                           0.041

Parameter Estimates:

  Standard errors                             Standard
  Information                                 Expected
  Information saturated (h1) model          Structured


Group 1 [yes]:

Regressions:
                        Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  ptgi_total_score ~                                                         
    nrtcsm   (gn1)         0.427    0.141    3.034    0.002    0.427    0.160
    extrvr   (ge1)         0.687    0.155    4.444    0.000    0.687    0.200
    opnnss   (go1)        -0.095    0.134   -0.706    0.480   -0.095   -0.026
    agrbln   (ga1)        -0.108    0.154   -0.700    0.484   -0.108   -0.026
    cnscnt   (gc1)         0.403    0.152    2.647    0.008    0.403    0.111
    mpss_t   (gm1)         0.206    0.068    3.008    0.003    0.206    0.112
  iesr_ts ~                                                                  
    nrtcsm   (in1)         0.424    0.085    4.985    0.000    0.424    0.245
    extrvr   (ie1)         0.345    0.093    3.696    0.000    0.345    0.154
    opnnss   (io1)         0.038    0.081    0.472    0.637    0.038    0.016
    agrbln   (ia1)        -0.112    0.093   -1.209    0.227   -0.112   -0.042
    cnscnt   (ic1)         0.209    0.092    2.273    0.023    0.209    0.089
    mpss_t   (im1)         0.018    0.041    0.435    0.664    0.018    0.015
  pos_self_compassion ~                                                      
    nrtcsm (pscn1)        -0.268    0.041   -6.614    0.000   -0.268   -0.267
    extrvr (psce1)         0.058    0.057    1.015    0.310    0.058    0.045
    opnnss (psco1)         0.103    0.049    2.087    0.037    0.103    0.074
    agrbln (psca1)         0.062    0.057    1.079    0.280    0.062    0.040
    cnscnt (pscc1)         0.015    0.054    0.287    0.774    0.015    0.011
    mpss_t (pscm1)         0.078    0.025    3.099    0.002    0.078    0.113
  neg_self_compassion ~                                                      
    nrtcsm (nscn1)         0.835    0.041   20.206    0.000    0.835    0.637
    extrvr (nsce1)        -0.166    0.058   -2.844    0.004   -0.166   -0.098
    opnnss (nsco1)         0.189    0.050    3.775    0.000    0.189    0.105
    agrbln (nsca1)        -0.142    0.058   -2.436    0.015   -0.142   -0.071
    cnscnt (nscc1)         0.101    0.055    1.849    0.064    0.101    0.057
    mpss_t (nscm1)        -0.025    0.026   -0.953    0.341   -0.025   -0.027
  active_coping ~                                                            
    nrtcsm  (acn1)        -0.085    0.030   -2.893    0.004   -0.085   -0.111
    extrvr  (ace1)         0.058    0.042    1.405    0.160    0.058    0.059
    opnnss  (aco1)         0.133    0.036    3.715    0.000    0.133    0.126
    agrbln  (aca1)        -0.021    0.042   -0.515    0.606   -0.021   -0.018
    cnscnt  (acc1)         0.324    0.039    8.292    0.000    0.324    0.311
    mpss_t  (acm1)         0.056    0.018    3.054    0.002    0.056    0.106
  avoidance_coping ~                                                         
    nrtcsm  (vcn1)         0.211    0.028    7.509    0.000    0.211    0.300
    extrvr  (vce1)         0.118    0.040    2.971    0.003    0.118    0.129
    opnnss  (vco1)        -0.030    0.034   -0.886    0.376   -0.030   -0.031
    agrbln  (vca1)        -0.115    0.040   -2.910    0.004   -0.115   -0.107
    cnscnt  (vcc1)        -0.180    0.037   -4.843    0.000   -0.180   -0.188
    mpss_t  (vcm1)        -0.007    0.018   -0.412    0.680   -0.007   -0.015
  ptgi_total_score ~                                                         
    ps_sl_  (gps1)         0.419    0.098    4.279    0.000    0.419    0.158
    ng_sl_  (gns1)         0.171    0.096    1.783    0.075    0.171    0.084
    actv_c  (gac1)         0.283    0.134    2.109    0.035    0.283    0.082
    avdnc_  (gav1)         0.258    0.141    1.830    0.067    0.258    0.068
  iesr_ts ~                                                                  
    ps_sl_  (ips1)         0.082    0.059    1.396    0.163    0.082    0.048
    ng_sl_  (ins1)         0.468    0.058    8.079    0.000    0.468    0.355
    actv_c  (iac1)        -0.026    0.081   -0.315    0.753   -0.026   -0.011
    avdnc_  (iav1)         0.132    0.085    1.546    0.122    0.132    0.053

Covariances:
                         Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
 .pos_self_compassion ~~                                                      
   .neg_slf_cmpssn         -0.591    2.549   -0.232    0.817   -0.591   -0.008
 .ptgi_total_score ~~                                                         
   .iesr_ts                67.292   11.065    6.082    0.000   67.292    0.228

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .ptgi_total_scr  -39.333   12.215   -3.220    0.001  -39.333   -1.664
   .iesr_ts           7.085    7.372    0.961    0.337    7.085    0.461
   .pos_slf_cmpssn   31.791    3.228    9.847    0.000   31.791    3.568
   .neg_slf_cmpssn  -56.610    3.292  -17.197    0.000  -56.610   -4.867
   .active_coping    28.498    2.353   12.112    0.000   28.498    4.188
   .avoidance_cpng   32.546    2.241   14.526    0.000   32.546    5.215

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .ptgi_total_scr  488.136   25.275   19.313    0.000  488.136    0.873
   .iesr_ts         177.828    9.208   19.313    0.000  177.828    0.754
   .pos_slf_cmpssn   68.267    3.535   19.313    0.000   68.267    0.860
   .neg_slf_cmpssn   70.980    3.675   19.313    0.000   70.980    0.525
   .active_coping    36.261    1.878   19.313    0.000   36.261    0.783
   .avoidance_cpng   32.883    1.703   19.313    0.000   32.883    0.844


Group 2 [no]:

Regressions:
                        Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  ptgi_total_score ~                                                         
    nrtcsm   (gn2)         0.102    0.138    0.739    0.460    0.102    0.044
    extrvr   (ge2)         0.367    0.180    2.037    0.042    0.367    0.115
    opnnss   (go2)         0.138    0.169    0.818    0.413    0.138    0.045
    agrbln   (ga2)         0.328    0.196    1.671    0.095    0.328    0.095
    cnscnt   (gc2)        -0.143    0.167   -0.859    0.390   -0.143   -0.049
    mpss_t   (gm2)         0.294    0.103    2.871    0.004    0.294    0.155
  iesr_ts ~                                                                  
    nrtcsm   (in2)         0.249    0.053    4.677    0.000    0.249    0.277
    extrvr   (ie2)         0.229    0.069    3.307    0.001    0.229    0.187
    opnnss   (io2)        -0.104    0.065   -1.598    0.110   -0.104   -0.088
    agrbln   (ia2)        -0.097    0.076   -1.284    0.199   -0.097   -0.073
    cnscnt   (ic2)         0.085    0.064    1.323    0.186    0.085    0.075
    mpss_t   (im2)        -0.046    0.040   -1.163    0.245   -0.046   -0.063
  pos_self_compassion ~                                                      
    nrtcsm (pscn2)        -0.113    0.060   -1.873    0.061   -0.113   -0.110
    extrvr (psce2)         0.223    0.082    2.706    0.007    0.223    0.159
    opnnss (psco2)         0.023    0.078    0.294    0.769    0.023    0.017
    agrbln (psca2)        -0.064    0.091   -0.701    0.483   -0.064   -0.042
    cnscnt (pscc2)        -0.002    0.076   -0.027    0.978   -0.002   -0.002
    mpss_t (pscm2)        -0.082    0.048   -1.700    0.089   -0.082   -0.097
  neg_self_compassion ~                                                      
    nrtcsm (nscn2)         0.349    0.069    5.049    0.000    0.349    0.285
    extrvr (nsce2)        -0.224    0.094   -2.379    0.017   -0.224   -0.134
    opnnss (nsco2)         0.014    0.089    0.162    0.871    0.014    0.009
    agrbln (nsca2)         0.009    0.104    0.088    0.930    0.009    0.005
    cnscnt (nscc2)        -0.086    0.087   -0.992    0.321   -0.086   -0.056
    mpss_t (nscm2)         0.100    0.055    1.812    0.070    0.100    0.100
  active_coping ~                                                            
    nrtcsm  (acn2)        -0.213    0.044   -4.866    0.000   -0.213   -0.267
    extrvr  (ace2)         0.125    0.060    2.087    0.037    0.125    0.114
    opnnss  (aco2)         0.155    0.056    2.742    0.006    0.155    0.146
    agrbln  (aca2)        -0.084    0.066   -1.275    0.202   -0.084   -0.071
    cnscnt  (acc2)         0.124    0.055    2.256    0.024    0.124    0.123
    mpss_t  (acm2)         0.049    0.035    1.415    0.157    0.049    0.075
  avoidance_coping ~                                                         
    nrtcsm  (vcn2)         0.164    0.063    2.591    0.010    0.164    0.133
    extrvr  (vce2)        -0.245    0.086   -2.846    0.004   -0.245   -0.146
    opnnss  (vco2)        -0.255    0.081   -3.134    0.002   -0.255   -0.157
    agrbln  (vca2)        -0.333    0.095   -3.506    0.000   -0.333   -0.182
    cnscnt  (vcc2)        -0.374    0.079   -4.723    0.000   -0.374   -0.240
    mpss_t  (vcm2)        -0.014    0.050   -0.269    0.788   -0.014   -0.013
  ptgi_total_score ~                                                         
    ps_sl_  (gps2)         0.149    0.130    1.150    0.250    0.149    0.066
    ng_sl_  (gns2)         0.103    0.113    0.904    0.366    0.103    0.054
    actv_c  (gac2)         0.835    0.162    5.143    0.000    0.835    0.287
    avdnc_  (gav2)         0.107    0.113    0.947    0.344    0.107    0.056
  iesr_ts ~                                                                  
    ps_sl_  (ips2)         0.060    0.050    1.191    0.234    0.060    0.068
    ng_sl_  (ins2)         0.107    0.044    2.446    0.014    0.107    0.145
    actv_c  (iac2)         0.101    0.063    1.615    0.106    0.101    0.090
    avdnc_  (iav2)         0.043    0.043    0.984    0.325    0.043    0.058

Covariances:
                         Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
 .pos_self_compassion ~~                                                      
   .neg_slf_cmpssn        -44.437    6.406   -6.937    0.000  -44.437   -0.419
 .ptgi_total_score ~~                                                         
   .iesr_ts                12.782    8.915    1.434    0.152   12.782    0.080

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .ptgi_total_scr  -31.771   15.425   -2.060    0.039  -31.771   -1.432
   .iesr_ts           3.846    5.944    0.647    0.518    3.846    0.449
   .pos_slf_cmpssn   41.446    4.822    8.594    0.000   41.446    4.225
   .neg_slf_cmpssn  -42.409    5.517   -7.688    0.000  -42.409   -3.641
   .active_coping    37.685    3.499   10.771    0.000   37.685    4.947
   .avoidance_cpng   72.336    5.046   14.336    0.000   72.336    6.170

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .ptgi_total_scr  413.823   32.614   12.689    0.000  413.823    0.841
   .iesr_ts          61.450    4.843   12.689    0.000   61.450    0.838
   .pos_slf_cmpssn   92.669    7.303   12.689    0.000   92.669    0.963
   .neg_slf_cmpssn  121.263    9.557   12.689    0.000  121.263    0.894
   .active_coping    48.773    3.844   12.689    0.000   48.773    0.840
   .avoidance_cpng  101.444    7.995   12.689    0.000  101.444    0.738

Defined Parameters:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    abgg1             0.546    0.149    3.654    0.000    0.546    0.186
    abgg2            -0.113    0.106   -1.067    0.286   -0.113   -0.023
    abig1             0.107    0.078    1.369    0.171    0.107    0.056
    abig2            -0.045    0.041   -1.100    0.271   -0.045   -0.024
    totalgg1          2.608    0.365    7.151    0.000    2.608    0.867
    totalgg2          1.915    0.435    4.403    0.000    1.915    0.725
    totalig1          1.135    0.216    5.264    0.000    1.135    0.575
    totalig2          0.415    0.168    2.479    0.013    0.415    0.441

cor(
  cbind(
    all_items$pos_self_compassion,
    all_items$neg_self_compassion,
    all_items$ptgi_total_score,
    all_items$iesr_ts
  )
) |> round(2)

      [,1]  [,2] [,3]  [,4]
[1,]  1.00 -0.29 0.17 -0.04
[2,] -0.29  1.00 0.09  0.34
[3,]  0.17  0.09 1.00  0.20
[4,] -0.04  0.34 0.20  1.00

library(semhelpinghands)

ci_boot <- standardizedSolution_boot_ci(fit2)

Error in standardizedSolution_boot_ci(fit2) : 
  Bootstrapping estimates not found. Was se = 'boot' or 'bootstrap'?

parameterEstimates(fit2, boot.ci.type="bca.simple")

lavaanPlot::lavaanPlot(model = fit2)

fitMeasures(fit2)